A New Feature Extraction Method for Ship-Radiated Noise Based on Improved CEEMDAN, Normalized Mutual Information and Multiscale Improved Permutation Entropy
Abstract
:1. Introduction
2. Basic Theory
2.1. ICEEMDAN
2.2. Mutual Information and Normalized Mutual Information
2.3. MIPE
2.4. The Proposed Feature Extraction Method
3. Simulation Results
3.1. Analysis of Artificial Signal Based on ICEEMDAN
3.2. Analysis of Artificial Signal Based on MIPE
4. Experimental Results
4.1. Feature Extraction Based on ICEEMDAN-norMI-MIPE
4.2. Ship Classification
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
- Wang, S.G.; Zeng, X.Y. Robust underwater noise targets classification using auditory inspired time-frequency analysis. Appl. Acoust. 2014, 78, 68–76. [Google Scholar] [CrossRef]
- Wang, Q.; Zeng, X.Y.; Wang, L.; Wang, H.T.; Cai, H.H. Passive moving target classification via spectra multiplication method. IEEE Signal Process Lett. 2017, 24, 451–455. [Google Scholar] [CrossRef]
- Bao, F.; Li, C.; Wang, X.L.; Wang, Q.F.; Du, S.P. Ship classification using nonlinear features of radiated sound: An approach based on empirical mode decomposition. J. Acoust. Soc. Am. 2010, 128, 206–214. [Google Scholar] [CrossRef] [PubMed]
- Yang, S.; Li, Z.S.; Wang, X.L. Ship recognition via its radiated sound: The fractal based approaches. J. Acoust. Soc. Am. 2002, 112, 172–177. [Google Scholar] [CrossRef] [PubMed]
- David, S.D.; Soledad, T.G.; Antonio, C.L.; Antonio, P.G. ShipsEar: An underwater vessel noise database. Appl. Acoust. 2016, 113, 64–69. [Google Scholar]
- Das, A.; Kumar, A.; Bahl, R. Marine vessel classification based on passive sonar data: The cepstrum-based approach. IET Radar Sonar Navig. 2013, 7, 87–93. [Google Scholar] [CrossRef]
- Chen, Z.; Li, Y.Y.; Liang, H.T.; Yu, J. Hierarchical cosine similarity entropy for feature extraction of ship-radiated noise. Entropy 2018, 20, 425. [Google Scholar] [CrossRef]
- Richardson, A.M.; Hodgkjiss, W.S. Bispectral analysis of underwater acoustic data. J. Acoust. Soc. Am. 1994, 96, 828–837. [Google Scholar] [CrossRef]
- Hinich, M.J.; Marandino, D.; Sullivan, E.J. Bispectrum of ship-radiated noise. J. Acoust. Soc. Am. 1989, 85, 1512–1517. [Google Scholar] [CrossRef]
- Pflug, L.A.; Loup, G.E.; Loup, J.W.; Fiell, R.L. Properties of high-order correlations and spectrum for bandlimited deterministic transients. J. Acoust. Soc. Am. 1992, 91, 975–988. [Google Scholar] [CrossRef]
- Hodges, R.P. Underwater Acoustics: Analysis, Design and Performance of Sonar, 1st ed.; Wiley: West Sussex, UK, 2010; pp. 183–184. ISBN 978-0-470-68875-5. [Google Scholar]
- Waite, A.D. Sonar for Practicing Engineers, 3rd ed.; Wiley: West Sussex, UK, 2002; pp. 126–127. ISBN 0471497509. [Google Scholar]
- Pincus, S.M. Approximate entropy as a measure of system complexity. Proc. Natl. Acad. Sci. USA 1991, 88, 2297–2301. [Google Scholar] [CrossRef] [PubMed]
- Richman, J.S.; Moorman, J.R. Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Physiol. 2000, 278, H2039–H2049. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Bandt, C.; Pompe, B. Permutation entropy: a natural complexity measure for time series. Phys. Rev. Lett. 2002, 88, 174102. [Google Scholar] [CrossRef] [PubMed]
- Humeau-Heurtier, A.; Wu, C.W.; Wu, S.D. Refined composite multiscale permutation entropy to overcome multiscale permutation entropy length dependence. IEEE Signal Process Lett. 2015, 22, 2364–2367. [Google Scholar] [CrossRef]
- Zunino, L.; Zanin, M.; Tabak, B.M. Forbidden patterns, permutation entropy and stock market inefficiency. Physica A 2009, 388, 2854–2864. [Google Scholar] [CrossRef]
- Li, Y.B.; Li, G.Y.; Yang, Y.T. A fault diagnosis scheme for planetary gearboxes using adaptive multi-scale morphology filter and modified hierarchical permutation entropy. Mech. Syst. Sig. Process. 2018, 105, 319–337. [Google Scholar] [CrossRef]
- Fadlallah, B.; Chen, B.D.; Keil, A.; Principe, J. Weighted-permutation entropy: A complexity measure for time series incorporating amplitude information. Phys. Rev. E 2013, 88, 022911. [Google Scholar] [CrossRef] [PubMed]
- Zunino, L.; Olivares, F.; Scholkmann, F.; Rosso, O.A. Permutation entropy based time series analysis: Equalities in the input signal can lead to false conclusions. Phys. Lett. A 2017, 381, 1883–1892. [Google Scholar] [CrossRef] [Green Version]
- Bian, C.; Qin, C.; Ma, Q.D.; Shen, Q. Modified permutation-entropy analysis of heartbeat dynamics. Phys. Rev. E 2012, 85, 021906. [Google Scholar] [CrossRef]
- Chen, Z.; Li, Y.A.; Liang, H.T.; Yu, J. Improved permutation entropy for measuring complexity of time series under noisy condition. Complexity 2019, 2019, 1403829. [Google Scholar] [CrossRef]
- Costa, M.; Goldberger, A.L.; Peng, C.K. Multiscale entropy analysis of biological signals. Phys. Rev. E 2005, 71, 021906. [Google Scholar] [CrossRef] [PubMed]
- Konstantin, D.; Dominique, Z. Variational Mode Decomposition. IEEE Trans. Signal Process. 2014, 62, 531–544. [Google Scholar]
- Huang, N.E.; Shen, Z.; Long, S.R.; Wu, M.C.; Shih, H.H.; Zheng, Q.; Yen, N.; Tung, C.C.; Liu, H.H. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. Lond. 1998, 454, 903–995. [Google Scholar] [CrossRef]
- Wu, Z.; Huang, N.E. Ensemble empirical mode decomposition: A noise-assisted data analysis method. Adv. Adapt. Data Anal. 2009, 1, 1–41. [Google Scholar] [CrossRef]
- Yeh, J.R.; Shieh, J.S.; Huang, N.E. Complementary ensemble empirical mode decomposition: a novel noise enhanced data analysis method. Adv. Adapt. Data Anal. 2010, 2, 135–156. [Google Scholar] [CrossRef]
- Torres, M.E.; Colominas, M.A.; Schlotthauer, G.; Flandrin, P. A complete ensemble empirical mode decomposition with adaptive noise. In Proceedings of the 2011 IEEE International Conference on Acoustics, Speech and Signal (ICASSP), Prague, Czech Republic, 22–27 May 2011; pp. 4144–4147. [Google Scholar]
- Kuai, M.; Cheng, G.; Pang, Y.; Li, Y. Research of planetary gear fault diagnosis based on permutation entropy of CEEMDAN and ANFIS. Sensors 2018, 18, 782. [Google Scholar] [CrossRef] [PubMed]
- Zhang, W.; Qu, Z.; Zhang, K.; Mao, W.; Ma, Y.; Fan, X. A combined model based on CEEMDAN and modified flower pollination algorithm for wind speed forecasting. Energ. Convers. Manag. 2017, 136, 439–451. [Google Scholar] [CrossRef]
- Han, J.; Mirko, V.D.B. Complete Ensemble Empirical Mode Decomposition for Seismic Time-Frequency Analysis. Geophysics 2013, 78, O9–O19. [Google Scholar] [CrossRef]
- Imani, M.; Ghoreishi, S.F.; Braga-Neto, U.M. Bayesian control of large MDPs with unknown dynamics in data-poor environments. In Proceedings of the 32nd Conference on Neural Information Processing Systems, Montreal, Canada, 2–8 December 2018. [Google Scholar]
- Wang, C.; Bernstein, A.; Boudec, J.L.; Paolone, M. Explicit conditions on exisitence and uniqueness of load-flow solutions in distribution networks. IEEE Trans. Smart Grid 2016, 9, 953–962. [Google Scholar] [CrossRef]
- Henderson, P.; Islam, R.; Bachman, P.; Pineau, J.; Precup, D.; Meger, D. Deep reinforcement learning that matters. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence, New Orleans, LA, USA, 2–7 February 2018. [Google Scholar]
- Li, Y.; Wang, L.; Li, X.; Yang, X. A novel linear spectrum frequency feature extraction technique for warship radio noise based on complete ensemble empirical mode decomposition with adaptive noise, duffing chaotic oscillator, and weighted-permutation entropy. Entropy 2019, 21, 507. [Google Scholar] [CrossRef]
- Colominas, M.A.; Schlotthauer, G.; Torres, M.E. Improved complete ensemble EMD: A suitable tool for biomedical signal processing. Biomed. Signal Process. 2014, 14, 19–29. [Google Scholar] [CrossRef]
- Li, X.L.; Li, C.W. Improved CEEMDAN and PSO-SVR modeling for near-infrared noninvasive glucose detection. Comput. Math. Methods Med. 2016, 2016, 8301962. [Google Scholar] [CrossRef] [PubMed]
- Li, Y.X.; Li, Y.A.; Chen, Z.; Chen, X. Feature extraction of ship-radiated noise based on permutation entropy of the intrinsic mode function with the highest energy. Entropy 2016, 18, 393. [Google Scholar] [CrossRef]
- Yang, H.; Zhao, K.; Li, G.H. A new ship-radiated noise feature extraction technique based on variational mode decomposition and fluctuation-based dispersion entropy. Entropy 2019, 21, 235. [Google Scholar] [CrossRef]
- Azami, H.; Escudero, J. Amplitude- and fluctuation-based dispersion entropy. Entropy 2018, 20, 210. [Google Scholar] [CrossRef]
- Kvålseth, T.O. On normalized mutual information: measure derivations and properties. Entropy 2017, 19, 631. [Google Scholar] [CrossRef]
- Aziz, W.; Arif, M. Multiscale Permutation Entropy of Physiological Time Series. In Proceedings of the 2005 Pakistan Section Multitopic Conference, Karachi, Pakistan, 24–25 December 2005; pp. 1–6. [Google Scholar]
- Chanwimalueang, T.; Mandic, D. Cosine similarity entropy: self-correlated-based complexity analysis of dynamical systems. Entropy 2017, 19, 652. [Google Scholar] [CrossRef]
- Sounds Recorded in Glacier Bay. Available online: https://www.nps.gov/glba/learn/nature/soundclips.htm?tdsourcetag=s_pctim_aiomsg (accessed on 24 June 2019).
- Specht, D.F. Probability neural networks and the polynomial Adaline as complementary techniques for classification. IEEE Trans. Neural Netw. 1990, 1, 111–121. [Google Scholar] [CrossRef]
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Chen, Z.; Li, Y.; Cao, R.; Ali, W.; Yu, J.; Liang, H. A New Feature Extraction Method for Ship-Radiated Noise Based on Improved CEEMDAN, Normalized Mutual Information and Multiscale Improved Permutation Entropy. Entropy 2019, 21, 624. https://doi.org/10.3390/e21060624
Chen Z, Li Y, Cao R, Ali W, Yu J, Liang H. A New Feature Extraction Method for Ship-Radiated Noise Based on Improved CEEMDAN, Normalized Mutual Information and Multiscale Improved Permutation Entropy. Entropy. 2019; 21(6):624. https://doi.org/10.3390/e21060624
Chicago/Turabian StyleChen, Zhe, Yaan Li, Renjie Cao, Wasiq Ali, Jing Yu, and Hongtao Liang. 2019. "A New Feature Extraction Method for Ship-Radiated Noise Based on Improved CEEMDAN, Normalized Mutual Information and Multiscale Improved Permutation Entropy" Entropy 21, no. 6: 624. https://doi.org/10.3390/e21060624
APA StyleChen, Z., Li, Y., Cao, R., Ali, W., Yu, J., & Liang, H. (2019). A New Feature Extraction Method for Ship-Radiated Noise Based on Improved CEEMDAN, Normalized Mutual Information and Multiscale Improved Permutation Entropy. Entropy, 21(6), 624. https://doi.org/10.3390/e21060624